Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/zencity/databricks-logs-reader/dbr-logsnpx skills add zencity/databricks-logs-reader --skill dbr-logsgit clone --depth 1 https://github.com/zencity/databricks-logs-readerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zencity/databricks-logs-reader/dbr-logs)<a href="https://agentmods.dev/skills/zencity/databricks-logs-reader/dbr-logs"><img src="https://agentmods.dev/badge/skills/zencity/databricks-logs-reader/dbr-logs.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00101 | $0.02043 |
| Opus 5 | $0.00051 | $0.01022 |
| Sonnet 5 | $0.00020 | $0.00409 |
| Haiku 4.5 | $0.00010 | $0.00204 |
Grade A, and why
dbr-logs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbr-logs: Fetch and Analyze Databricks Job Logs
Follow these steps to fetch, analyze, and explain Databricks job logs.
Step 0: Ensure CLI is available
Check if the dbr-logs CLI is accessible. Try each tier in order:
which dbr-logs
- Found -> use
dbr-logsdirectly - Not found -> check for
uvx:which uvx- If
uvxavailable -> useuvx --from dbr-logs dbr-logs <args>for all commands below - If
uvxnot available -> ask the user:dbr-logsCLI not found. Install options:uv tool install dbr-logspip install dbr-logs
Want me to install it?
- If user declines -> fall back to raw
databricks fs ls/databricks fs catcommands. Warn: "Using raw Databricks CLI (no log merging or filtering). Install dbr-logs for a better experience." Loadreferences/log-structure.mdfor directory layout guidance.
- If
For the rest of these instructions, DBR_LOGS refers to whichever invocation method was resolved above (dbr-logs, uvx --from dbr-logs dbr-logs, etc.).
Step 1: Resolve the target job
- If the user provides a job name -> use it directly
- If the user provides a Databricks URL -> pass the full URL as the positional argument (the CLI parses job/run from it)
- If the user describes a failure without naming a job -> ask which job to investigate
- If the user specifies a source (e.g. "check executor logs", "look at the driver") -> use
--sourceaccordingly - Default environment is
prod. Only add--env <env>if the user specifies a different environment.
Step 2: Fetch logs via CLI
Run DBR_LOGS with appropriate flags. Always use --format jsonl when you (Claude) are consuming the output — structured data is easier to analyze. Use --format text only when the user wants raw output displayed directly.
Priority: match the user's intent. If the user asks to search for a specific string or pattern, pipe the output to grep rather than adding --level filtering — the match may appear at any log level (INFO, DEBUG, etc.). Only default to --level ERROR,WARN when the user asks about failures/errors without specifying what to search for. Similarly, if the user specifies a source (e.g. "executor logs"), honor that with --source rather than fetching all sources.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 154 lines · 101 tokens per session scan A 0c225fd9cf53
dbr-logs is a skill published in the GitHub repository zencity/databricks-logs-reader (2 stars, last pushed 2mo ago), licensed MIT. It adds 101 tokens to every session and 2,043 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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